Weather forecasts are often provided with relatively course geospatial and temporal resolution. For example, users are often provided weather forecasts for the nearest major metropolitan area to their location of interest. Although the rate at which the forecasts are updated may vary, often, the forecasts are only updated hourly. Many forecasts that are delivered to end users are provided by the National Weather Service or other private vendors. While updating the forecast more often and/or reducing the geospatial distance between forecasted locations would be ideal, much of the information would go unused and thus, would result in an excessive amount of computation that is wasted. Consequently, weather forecasts are currently generated in “bulk”, meaning a system generates forecasts for many cities, for example 10,000 globally and does so hourly. Although some systems may update more often or include improved geospatial resolution, the forecasts generally do not update instantly or correspond to a user's special location. Providing such resolution would be impractical in most situations. Imagine the computer power required to generate forecasts every minute at one meter spacing globally.
There exist certain situations where more detailed and refined weather information (current conditions or forecasts) is desired. The need for more detailed and updated weather information has become more prevalent with the increased use of mobile devices capable of receiving weather information over wireless internet connections or cellular connections. With the increased ability to receive instantaneous information, users continually desire weather information that is up to date and relevant to their special location.
The prior art systems that are currently available simply provide a user with existing data from an existing forecast, such as one provided by the National Weather Service or one created by the prior art system without any input by the user and forward the existing data to users without providing additional computations to update the forecast. Therefore, the weather information provided to the user is relatively generic and may not provide the specially desired by the user. In the climatological sciences, information is used for a variety of purposes. These include long-term forecasting for which climatology is often the best achievable estimate of weather conditions, downscaling of numerical weather forecast models, as a baseline for estimates of forecast skill, and the like. Climatological information is generally obtained by storing and analyzing observations from weather stations over a sufficiently long period of time. Traditionally, this has been done at fixed locations, since the purpose is to obtain a long-term statistically valid data set of average weather conditions. In some cases, estimates have been made of climatology at locations where sensors are not available, using a variety of techniques. Two examples are the PRISM data (http://www.prism.oregonstate.edu) and WorldClim (http://www.worldclim.org). These climatology grids have spatial resolution of 0.5-−1 km or more.
What is needed is an improved climatology, with better spatial resolution, in order to improve the accuracy of weather forecasts. Spatial resolution may be dramatically improved by increasing the geographic dispersal of sensors, which may be achieved through the use of mobile sensors.
The embodiments described below overcome these and other problems and an advance in the art is achieved. The embodiments described below obtain a gridded forecast with a fixed geospatial resolution and preset refresh intervals. Upon receiving a user request for weather information for a particular location and timeframe, a new updated forecast is determined based on data from updated sensor inputs and interpolations between forecast locations provided by the gridded forecast.